Sorting systems in the field of waste recycling – and in the sorting of plastics, in particular – is a fascinating topic. Here, high-tech computer control is combined with physical analytical methods in order, for example, to permit the separation and recycling into products of equivalent quality of plastics of differing composition. There is now a new route to the obtainment of even purer sorted fractions and the optimisation of the sorting process.
recovery magazine had the opportunity of taking a closer look at the new system – Dr. Petra Strunk, Editor-in-Chief of recovery, spoke to Technical Manager Dr. rer. nat. Daniel Bender and Product Manager Philipp Knopp, on the application potentials for artificial intelligence in sorting machines.↓
recovery: What was the impulse for the development of GAIN and for the use of the Deep Learning methods?
Dr. Daniel Bender: In practically all recycling facilities, there are certain situations in which people perform the sorting work. Humans are more capable of solving certain tasks than our classical sorting machines, because they have an understanding of the objects to be sorted and – where these objects can be visually differentiated – are able to make a dependable decision as to whether an item needs to be diverted out or not. These are usually jobs which do not provide any particularly good working environment, however, they are very strenuous and can also, in some cases, be hazardous. Costs, speed and consistency are also criteria arguing in favour of a machine-based solution. that‘s where TOMRA wanted to start. In 2012, the results of international research demonstrated that Deep Learning can enable visual identification of objects at accuracy levels similar to those of a human being. We started developing GAIN in 2016, and a dedicated team was set up for this purpose in 2018. TOMRA provides us with a lot of support, and the sector is growing rapidly – with the aim of further expanding the range of applications.
recovery: How does the GAIN module work in the sorting process?
Dr. Daniel Bender: The AUTOSORT system analyses the plastics in the NIR (near-infrared) range and separates PE plastics from the remaining materials correspondingly. The PE cartridges are not diverted out, since they consist, superficially, of material detected as „good“. Now another sensor-based system – GAIN – is implemented, with a camera which, like humans, processes information from the visible spectrum of light. This is, in hardware terms, a relatively simple system – it‘s the software that makes the difference. At the end, the undesirable materials identified both by the AUTOSORT and by the GAIN module are blown out by arrays of air jets.
recovery: Will there possibly be even more application for GAIN in the future?
Dr. Daniel Bender: Yes, there will be further applications. The rule of thumb for use with cameras is that all objects that a human being can distinguish in the camera image can also be sorted by sensor-based sorting with a Deep Learning System. However, the effort of learning GAIN is quite high, i.e. for single applications Deep Learning methods are not yet suitable in their current form.
recovery: How complex is it to install this new module?
Philipp Knopp: GAIN can be very easily retrofitted
to AUTOSORT machines in one working day. In the development we paid special attention to the fact that GAIN is a modular, flexible system.
recovery: Does the user need any special knowledge?
Philipp Knopp: Since TOMRA develops the used technologies in-house and also manufactures the machines within the company, we can react quickly and systematically to customer enquiries.
recovery: How do you perceive future development in the field of Deep Learning?
Philipp Knopp: We see huge potential in Deep Learning. Finding new technologies for advancing the recycling industry is indeed our field of expertise. From packaging, via film sorting, wood, minerals – every technology has its limits, but the Deep Learning method has the potential to expand these applications to permit much more accurate sorting.
recovery: Many thanks to you for this highly interesting talk!